We introduce the Rel-grams language model, which is analogous to an
n-grams model, but is computed over relations rather than over words.
The model encodes the conditional probability of observing a
relational tuple R, given that R' was observed in a window of
prior relational tuples. We build a database of rel-grams co-occurence
statistics from ReVerb extractions over 1.8M news wire documents and
show that a graphical model based on these statistics is useful for
automatically discovering event templates. We make this database
freely available and hope it will prove a useful resource for a wide
variety of NLP tasks.